The present technology relates to a content recommendation device, a recommended content search method and a program.
In recent years, businesses using networks are growing fast. For example, systems such as online stores and the like where products can be purchased online are widely used. Many of these online stores use a mechanism of recommending products to users. For example, when a user views detailed information of a product, information on products related to the product is presented to the user as recommended products.
Such a mechanism is realized by using a method such as collaborative filtering described in JP 2003-167901A, for example. This collaborative filtering is a method of automatically giving recommendation by using information of a user with similar preference, based on preference information of many users. When using this collaborative filtering, a recommendation result can be provided also to a new user with no purchase history.
Furthermore, a method called content-based filtering may also be used for recommendation of a product. This content-based filtering is a method of matching an attribute of content and the taste of a user and thereby recommending related content. According to this content-based filtering, a highly accurate recommendation result can be provided, compared to collaborative filtering, even in a situation where the number of users using a recommendation system is small. However, in a situation where information for identifying content that a target user likes (for example, a purchase history, content meta-information or the like) is scarce, it is difficult to obtain a highly accurate recommendation result using content-based filtering.